Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Investigating gene methylation signatures for fetal intolerance prediction

Pregnancy is a complicated and long procedure during one or more offspring development inside a woman. A short period of oxygen shortage after birth is quite normal for most babies and does not threaten their health. However, if babies have to suffer from a long period of oxygen shortage, then this condition is an indication of pathological fetal intolerance, which probably causes their death. The identification of the pathological fetal intolerance from the physical oxygen shortage is one of the important clinical problems in obstetrics for a long time. The clinical syndromes typically manifest five symptoms that indicate that the baby may suffer from fetal intolerance. At present, liquid biopsy combined with high-throughput sequencing or mass spectrum techniques provides a quick approach to detect real-time alteration in the peripheral blood at multiple levels with the rapid development of molecule sequencing technologies. Gene methylation is functionally correlated with gene expression; thus, the combination of gene methylation and expression information would help in screening out the key regulators for the pathogenesis of fetal intolerance. We combined gene methylation and expression features together and screened out the optimal features, including gene expression or methylation signatures, for fetal intolerance prediction for the first time. In addition, we applied various computational methods to construct a comprehensive computational pipeline to identify the potential biomarkers for fetal intolerance dependent on the liquid biopsy samples. We set up qualitative and quantitative computational models for the prediction for fetal intolerance during pregnancy. Moreover, we provided a new prospective for the detailed pathological mechanism of fetal intolerance. This work can provide a solid foundation for further experimental research and contribute to the application of liquid biopsy in antenatal care.

Identifying Infliximab- (IFX-) Responsive Blood Signatures for the Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a severe chronic pathogenic inflammatory abnormality that damages small joints. Comprehensive diagnosis and treatment procedures for RA have been established because of its severe symptoms and relatively high morbidity. Medication and surgery are the two major therapeutic approaches. Infliximab (IFX) is a novel biological agent applied for the treatment of RA. IFX improves physical functions and benefits the achievement of clinical remission even under discontinuous medication. However, not all patients react to IFX, and distinguishing IFX-sensitive and IFX-resistant patients is quite difficult. Thus, how to predict the therapeutic effects of IFX on patients with RA is one of the urgent translational medicine problems in the clinical treatment of RA. In this study, we present a novel computational method for the identification of the applicable and substantial blood gene signatures of IFX sensitivity by liquid biopsy, which may assist in the establishment of a clinical drug sensitivity test standard for RA and contribute to the revelation of unique IFX-associated pharmacological mechanisms.

Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

Identifying the Immunological Gene Signatures of Immune Cell Subtypes

The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high-throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte-Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two-stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition.